Physiologically Based Pharmacokinetic Modeling Approach Research Articles

Applications of linking PBPK and PD models to predict the impact of genotypic variability, formulation differences, differences in target binding capacity and target site drug concentrations on drug responses and variability.

This study aimed to demonstrate the added value of integrating prior in vitro data and knowledge-rich physiologically based pharmacokinetic (PBPK) models with pharmacodynamics (PDs) models. Four distinct applications that were developed and tested are presented here. PBPK models were developed for metoprolol using different CYP2D6 genotypes based on in vitro data. Application of the models for prediction of phenotypic differences in the pharmacokinetics (PKs) and PD compared favorably with clinical data, demonstrating that these differences can be predicted prior to the availability of such data from clinical trials. In the second case, PK and PD data for an immediate release formulation of nifedipine together with in vitro dissolution data for a controlled release (CR) formulation were used to predict the PK and PD of the CR. This approach can be useful to pharmaceutical scientists during formulation development. The operational model of agonism was used in the third application to describe the hypnotic effects of triazolam, and this was successfully extrapolated to zolpidem by changing only the drug related parameters from in vitro experiments. This PBPK modeling approach can be useful to developmental scientists who which to compare several drug candidates in the same therapeutic class. Finally, differences in QTc prolongation due to quinidine in Caucasian and Korean females were successfully predicted by the model using free heart concentrations as an input to the PD models. This PBPK linked PD model was used to demonstrate a higher sensitivity to free heart concentrations of quinidine in Caucasian females, thereby providing a mechanistic understanding of a clinical observation. In general, permutations of certain conditions which potentially change PK and hence PD may not be amenable to the conduct of clinical studies but linking PBPK with PD provides an alternative method of investigating the potential impact of PK changes on PD.

A Physiologically Based Modeling Strategy during Preclinical CNS Drug Development

Physiologically based pharmacokinetic (PBPK) modeling of the central nervous system (CNS) provides the opportunity to predict the relevant drug concentrations at the therapeutic target site during preclinical and clinical development. In order to successfully interpret model results, and to provide confidence in the subsequent human predictions, it is essential that an appropriate model structure is chosen at the preclinical stage which takes into account both physiological and drug-specific knowledge. However, the models published to date in the literature show significant variation in the approaches applied by different authors, which can lead to difficulties in the interpretation of model parameter estimates. We aimed to develop a coherent PBPK modeling approach in the rat, which would also be adaptable depending on the quantity and quality of in vivo data obtained during drug development. Based on a sensitivity analysis of the model parameters, and using three CNS drugs as case studies (atomoxetine, acetaminophen, and S 18986), we proposed a decision tree to aid in the appropriate parametrization and structure of the model according to the data available. We compared our parameter estimates to those originally published, and considered the impact of the respective approaches on the mechanistic interpretation of the parameter values. Since the measurement of brain extracellular fluid (ECF) concentrations using microdialysis is not routinely performed in the industrial environment, we also evaluated the bottom-up scaling of in vitro permeability data from the Caco-2 cell line to predict BBB passive permeability in the absence of measured ECF concentrations. Our strategy demonstrates the value of PBPK as a prediction tool throughout the development process of CNS-targeting drugs.

Physiologically based pharmacokinetic modeling of POPs in Greenlanders

Human exposure to persistent organic pollutants (POPs) and the potential health impact in the Arctic far from the emission sources have been highlighted in numerous studies. As a supplement to human POP biomonitoring studies, a physiologically based pharmacokinetic (PBPK) model was set up to estimate the fate of POPs in Greenlandic Inuit's liver, blood, muscle and adipose tissue following long-term exposure to traditional Greenlandic diet. The PBPK model described metabolism, excretion and POP accumulation on the basis of their physicochemical properties and metabolic rates in the organisms. Basic correlations between chemically analyzed blood POP concentrations and calculated daily POP intake from food questionnaire of 118 middle age (18–35years) Greenlandic Inuits from four cities in West Greenland (Qaanaaq: n=40; Qeqertarsuaq: n=36; Nuuk: n=20; Narsaq: n=22) taken during 2003 to 2006 were analyzed. The dietary items included were polar bear, caribou, musk oxen, several marine species such as whales, seals, bird and fish as well as imported food. The contaminant concentrations of the dietary items as well as their chemical properties, uptake, biotransformation and excretion allowed us to estimate the POP concentration in liver, blood, muscle and adipose tissue following long-term exposure to the traditional Greenlandic diet using the PBPK model. Significant correlations were found between chemically analyzed POP blood concentrations and calculated daily intake of POPs for Qeqertarsuaq, Nuuk and Narsaq Inuit but not for the northernmost settlement Qaanaaq, probably because the highest blood POP level was found in this district which might mask the interview-based POP calculations. Despite the large variation in circulating blood POP concentrations, the PBPK model predicted blood concentrations of a factor 2–3 within the actual measured values. Moreover, the PBPK model showed that estimated blood POP concentration increased significantly after consumption of meals. For individuals who had a high internal burden of POPs accumulated over years, the estimated blood levels were less influenced by recent meal intake. The model results also indicated that of the POPs accumulated in the body the concentrations were highest for CB-153 (oxychlordane: 0.6%; DDE and CB-99: 2.9%; HCB: 4.4%; CB-153: 34.5%). Furthermore, the model also estimated a significant internal body POP burden even several years after the mentioned dietetic shift and that contaminant accumulation was 2–6 folds faster than the decay after a shift to a diet low in contaminants. Using the PBPK model approach, we seek to improve the knowledge on contaminant body burden in humans of the Arctic. However, it should be noted that calculations of daily POP intake may be subject to considerable uncertainty due to imprecise information from the dietary interview. Based on these results we suggest that PBPK modeling is implemented as a tool in future human health exposure and effect assessments in Greenland.

Population pharmacokinetics meets microdialysis: Benefits, pitfalls and necessities of new analysis approaches for human microdialysis data

Pharmacokinetic (PK) data originating from human microdialysis studies have by now most commonly been analysed using noncompartmental analysis or the standard two-stage population approach. During the last decades, additional modelling strategies for PK data have been developed, which also seem apt for the analysis of microdialysis data. The present opinion article gives an overview of the three approaches of nonlinear mixed-effects (NLME) modelling, physiologically-based pharmacokinetic (PBPK) modelling and a combined population PBPK modelling approach (PPBPK) as well as their application within the field of microdialysis in humans. Potential benefits and pitfalls of the different approaches will be outlined and exemplified, complemented by modelling prerequisites to be met on the part of principal investigators, bioanalysts and documentalists involved in a microdialysis study.In summary, the combination of microdialysis as the method of choice for measuring unbound drug concentrations in peripheral tissues and the presented modelling strategies seems a promising way to enhance the understanding of drug disposition at the target site of drugs and might thus contribute to a more rational use of medicines.

Physiologically Based Pharmacokinetic Models for the Optimization of Antiretroviral Therapy: Recent Progress and Future Perspective

Anti-HIV therapy is characterized by the chronic administration of antiretrovirals (ARVs), and consequently, several problems can arise during the management of HIV-positive patients. ARV disposition can be simulated by combining system data describing a population of patients and in vitro drug data through physiologically based pharmacokinetic (PBPK) models, which mathematically describe absorption, distribution, metabolism and elimination. PBPK modeling can find application in the investigation of clinically relevant scenarios, while providing the opportunity for a better understanding of the mechanisms regulating drug distribution. In this review, we have analyzed the most recent applications of PBPK models for ARVs and highlighted some of the most interesting areas of use, such as drug–drug interaction, pharmacogenetics, factors regulating absorption and tissue penetration, as well as therapy optimization in special populations. The application of the PBPK modeling approach might not be limited to the investigation of hypothetical clinical issues, but could be used to inform future prospective clinical trials.

Semi-mechanistic physiologically-based pharmacokinetic modeling of clinical glibenclamide pharmacokinetics and drug–drug-interactions

We studied if the clinical pharmacokinetics and drug–drug interactions (DDIs) of the sulfonylurea-derivative glibenclamide can be simulated via a physiologically-based pharmacokinetic modeling approach. To this end, a glibenclamide PBPK-model was build in Simcyp using in vitro physicochemical and biotransformation data of the drug, and was subsequently optimized using plasma disappearance data observed after i.v. administration. The model was validated against data observed after glibenclamide oral dosing, including DDIs. We found that glibenclamide pharmacokinetics could be adequately modeled if next to CYP metabolism an active hepatic uptake process was assumed. This hepatic uptake process was subsequently included in the model in a non-mechanistic manner. After an oral dose of 0.875mg predicted Cmax and AUC were 39.7 (95% CI:37.0–42.7)ng/mL and 108 (95% CI: 96.9–120)ng/mLh, respectively, which is in line with observed values of 43.6 (95% CI: 37.7–49.5)ng/mL and 133 (95% CI: 107–159)ng/mLh. For a 1.75mg oral dose, the predicted and observed values were 82.5 (95% CI:76.6–88.9)ng/mL vs 91.1 (95% CI: 67.9–115.9) for Cmax and 224 (95% CI: 202–248) vs 324 (95% CI: 197–451)ng/mLh for AUC, respectively. The model correctly predicted a decrease in exposure after rifampicin pre-treatment. An increase in glibenclamide exposure after clarithromycin co-treatment was predicted, but the magnitude of the effect was underestimated because part of this DDI is the result of an interaction at the transporter level. Finally, the effects of glibenclamide and fluconazol co-administration were simulated. Our simulations indicated that co-administration of this potent CYP450 inhibitor will profoundly increase glibenclamide exposure, which is in line with clinical observations linking the glibenclamide-fluconazol combination to an increased risk of hypoglycemia. In conclusion, glibenclamide pharmacokinetics and its CYP-mediated DDIs can be simulated via PBPK-modeling. In addition, our data underline the relevance of modeling transporters on a full mechanistic level to further improve pharmacokinetic and DDI predictions of this sulfonylurea-derivative.

Predicting human exposure of active drug after oral prodrug administration, using a joined in vitro/in silico–in vivo extrapolation and physiologically-based pharmacokinetic modeling approach

Introduction: Predicting the pharmacokinetics (PK) of prodrugs and their corresponding active drugs is challenging, as there are many variables to consider. Prodrug conversion characteristics in different tissues are generally measured, but integrating these variables to a PK profile is not a common practice. In this paper, a joined in vitro/in silico–in vivo extrapolation (IVIVE) and physiologically-based pharmacokinetic (PBPK) modeling approach is presented to predict active drug exposure in human after oral prodrug administration. Methods: Physico-chemical and in vitro assays as well as in silico predictions were proposed to characterize key pharmacokinetic properties (e.g. clearance, volume of distribution, conversion rates) of three marketed prodrugs. These data were used to parameterize a PBPK model for simulating human PK profiles of the active drugs after prodrug administration, which were compared to literature data by evaluating the accuracy and uncertainty of the predictions. Results: For mycophenate mofetil and midodrine the PK of their active moieties could be adequately predicted. The assumptions of the PBPK–IVIVE approach were valid, i.e. being hepatically cleared, converted in the gut lumen, blood and liver and not metabolized in the gut wall. However, the observed profiles after oral bambuterol administration clearly fell outside the prediction interval as the PBPK model failed to predict the observed bioavailability. Discussion: Adding quantitative information about prodrug conversion in the gut, liver and blood to a PBPK model for the absorption, distribution, metabolism and excretion (ADME) properties of prodrugs and their active moieties resulted, retrospectively, in reasonable predictions of the human PK when the ADME properties are well understood. Also in a prospective compound selection process, this integrative approach can improve decision making on prodrug candidates by putting relative differences in prodrug conversion of a large number of candidates into the perspective of their human PK profile, before conducting any in vivo experiments.

An integrated QSPR–PBPK modelling approach for in vitro–in vivo extrapolation of pharmacokinetics in rats†

In vitro data on metabolism and partitioning may be integrated within physiologically-based pharmacokinetic (PBPK) models to provide simulations of the kinetics and bioaccumulation of chemicals in intact organisms. Quantitative structure–property relationship (QSPR) modelling of available in vitro data may be performed to predict metabolism rates and partition coefficients (PCs) for developing in vivo PBPK models. The objective of the present study was to develop an integrated QSPR–PBPK modelling approach for the conduct of in vitro to in vivo extrapolation. For this purpose, data on rat blood:air (P b) and fat:air (P f) PCs, as well as intrinsic metabolic clearance (CLint) obtained using rat liver slices for some C5–C10 volatile organic compounds (VOCs) were compiled from the literature. Multilinear additive QSPR models for P f, P b and CLint were developed based on the number and nature of molecular fragments in these VOCs (CH3, CH2, CH, C, C=C, H, benzene ring and H in benzene ring structure). The mean estimated/experimental (est/exp) ratios (±SD; range) were 1.0 (±0.04; 0.93 − 1.06) for log P f, 1.08 (±0.26; 0.70 − 1.62) for log P b, and 1.07 (± 0.21; 0.80 − 1.44) for CLint. By accounting for the difference in the content of neutral lipids in fat and other tissues, the liver : air and muscle : air PCs of the compounds investigated in this study, with the excerption of n-decane, were adequately predicted from P f. Integrating the QSPRs for P f, P b and CLint within a rat PBPK model, simulations of inhalation pharmacokinetics of several VOCs were generated on the basis of molecular structure, for a given exposure scenario. The integrated QSPR–PBPK model developed in this study is a potentially useful tool for predicting in vivo kinetics and bioaccumulation of chemicals in rats under poor data situations. †Presented at the 13th International Workshop on QSARs in the Environmental Sciences (QSAR 2008), 8–12 June 2008, Syracuse, USA.

New Window into Breast Cancer Risk: Assessing Lifetime Exposures to POPs

Persistent organic pollutants (POPs) such as polychlorinated biphenyls (PCBs) are ubiquitous chemical compounds that persist in the environment and bioaccumulate through the food web. Although experiments have shown that POPs stimulate the proliferation of human cancer cell lines, epidemiologic studies of POP-associated cancer risk have yielded inconsistent results, possibly because of the lack of tools for estimating lifetime exposures to these chemicals. Now, however, researchers have developed a new physiologically based pharmacokinetic (PBPK) modeling approach that can potentially be used in epidemiologic studies to simulate lifetime toxicokinetics of POPs in women [EHP 116:886–892; Verner et al.]. Previous biological assessments have been limited to measuring POP levels in blood or tissue samples collected around the time of breast cancer diagnosis. However, such assessments may not reflect the body burden during earlier, potentially critical exposure points in a woman’s life such as the fetal, postnatal, and adolescent periods. In contrast, the new model integrates the relevant processes of absorption, distribution, metabolism, and elimination to estimate lifetime blood and tissue exposure and levels during any hypothesized time window of susceptibility in breast cancer development. The model also predicts how various types of relevant lifetime physiologic changes—such as body weight variation, pregnancy, excretion of POPs through lactation, and aging—will influence the kinetics of a compound in a woman throughout her life. The model enables the estimation of interindividual differences in POP exposures through the use of physiologic information obtained from questionnaires in epidemiologic studies. The researchers found that lactation and weight change histories had the greatest impact on the toxicokinetic profile throughout life. According to the model, the longer and later in life lactation occurred, the lower the woman’s blood POP concentration at age 55 (a surrogate time representing the typical age at breast cancer diagnosis). Similarly, variations in body weight throughout life had a greater impact than average body weight on blood POP concentrations, possibly because weight loss is accompanied by unloading of POPs into the blood via lost adipose tissue. This means that quantitative information on both lactation and body weight histories is critical when evaluating past POP exposures. If, as some researchers hypothesize, breast cancer is related to POP exposures at specific time windows of susceptibility during a woman’s lifetime, lactation and body weight histories must be considered in studies of POP exposures and breast cancer risk. Depending on when such physiologic events occur, women having similar POP concentrations at the age of diagnosis may have had completely different internal levels at a time that may be critical to the formation of breast cancer. The proposed PBPK modeling approach therefore could be used in environmental epidemiology research to circumvent limitations inherent in relying on late-life sampling for past exposure assessments.

Moving From External Exposure Concentration to Internal Dose: Duration Extrapolation Based on Physiologically Based Pharmacokinetic Derived Estimates of Internal Dose

The potential human health risk(s) from chemical exposure must frequently be assessed under conditions for which adequate human or animal data are not available. The default method for exposure-duration adjustment, based on Haber's rule, C (external exposure concentration) or C n (the ten Berge modification) × t (exposure duration) = K (a constant toxic effect), has been criticized for prediction errors. A promising alternative approach to duration adjustment is based on equivalence of internal dose, that is, target-tissue dose levels, across different exposure durations. A proposed methodology for dose-duration adjustments for acute exposure guideline levels (AEGLs) based on physiologically based pharmacokinetic (PBPK) estimates of dose is illustrated with trichloroethylene (TCE). Steps in this methodology include: (1) selection and evaluation, or development and evaluation, of an appropriate PBPK model; (2) determination of an appropriate measure of internal dose; (3) estimation with the PBPK model of the tissue dose (the target tissue dose) resulting from the external exposure conditions (concentration, duration) of the critical effect; (4) estimation of the external exposure concentrations required to achieve tissue doses equivalent to the target tissue dose at exposure durations of interest; and (5) evaluation of sources of variability and uncertainty. For TCE, this PBPK modeling approach has allowed determination of dose metrics predictive of the acute neurotoxic effects of TCE and dose-duration adjustments based on estimates of internal dose.

Clinical pharmacokinetic/pharmacodynamic and physiologically based pharmacokinetic modeling in new drug development: the capecitabine experience.

Preclinical studies, along with Phase I, II, and III clinical trials demonstrate the pharmacokinetics, pharmacodynamics, safety and efficacy of a new drug under well controlled circumstances in relatively homogeneous populations. However, these types of studies generally do not answer important questions about variability in specific factors that predict pharmacokinetic and pharmacodynamic (PKPD) activity, in turn affecting safety and efficacy. Semi-physiological and clinical PKPD modeling and simulation offer the possibility of utilizing data obtained in the laboratory and the clinic to make accurate characterizations and predictions of PKPD activity in the target population, based on variability in predictive factors. Capecitabine is an orally administered pro-drug of 5-fluorouracil (5-FU), designed to exploit tissue-specific differences in metabolic enzyme activities in order to enhance efficacy and safety. It undergoes extensive metabolism in multiple physiologic compartments, and presents particular challenges for predicting pharmacokinetic and pharmacodynamic activity in humans. Clinical and physiologically based pharmacokinetic (PBPK) and pharmacodynamic models were developed to characterize the activity of capecitabine and its metabolites, and the clinical consequences under varying physiological conditions such as creatinine clearance or activity of key metabolic enzymes. The results of the modeling investigations were consistent with capecitabine's rational design as a triple pro-drug of 5-FU. This paper reviews and discusses the PKPD and PBPK modeling approaches used in capecitabine development to provide a more thorough understanding of what the key predictors of its PBPK activity are, and how variability in these predictors may affect its PKPD, and ultimately, clinical outcomes.

Whole body pharmacokinetic models.

The aim of the current review is to summarise the present status of physiologically based pharmacokinetic (PBPK) modelling and its applications in drug research, and thus serve as a reference point to people interested in the methodology. The review is structured into three major sections. The first discusses the existing methodologies and techniques of PBPK model development. The second describes some of the most interesting PBPK model implementations published. The final section is devoted to a discussion of the current limitations and the possible future developments of the PBPK modelling approach. The current review is focused on papers dealing with the pharmacokinetics and/or toxicokinetics of medicinal compounds; references discussing PBPK models of environmental compounds are mentioned only if they represent considerable methodological developments or reveal interesting interpretations and/or applications.The major conclusion of the review is that, despite its significant potential, PBPK modelling has not seen the development and implementation it deserves, especially in the drug discovery, research and development processes. The main reason for this is that the successful development and implementation of a PBPK model is seen to require the investment of significant experience, effort, time and resources. Yet, a substantial body of PBPK-related research has been accumulated that can facilitate the PBPK modelling and implementation process. What is probably lagging behind is the expertise component, where the demand for appropriately qualified staff far outreaches availability.

Physiologically-based pharmacokinetic modeling and bioactivation of xenobiotics.

This paper describes the development and implementation of physiologically-based pharmacokinetic (PB-Pk) models to examine the disposition of xenobiotics and their bioactivation. In a PB-Pk model, the structure of the model is based, to as great extent as practicable, on the actual physiological and biochemical structure of the animal system being described. This paper provides an overview of the PB-Pk modeling approach using a series of models as examples. PB-Pk models for styrene and the dihalomethanes are discussed in relation to their ability to predict the kinetics of uptake, distribution, metabolism (bioactivation), and elimination in both rodents and humans. Three models are discussed which demonstrate the process of describing increasing complexity in bioactivation with reference to saturation of metabolism (methylene chloride), suicide enzyme inactivation (trans-1,2-dichloroethylene), and glutathione depletion (allyl chloride). Experimental studies to quantify these particular examples of non-linear kinetics were conducted by closed chamber gas uptake techniques. All of these behaviors can be quantitatively expressed within the framework of a PB-Pk model.

Planning and using PB-PK models: an integrated inhalation and distribution model for nickel

A general method is presented for the use of mathematical modeling in the design, execution, and interpretation of toxicology experiments. To illustrate the use of mathematical modeling toxicology, a case study is presented of how a dosimetry model for inhaled nickel was developed for use in cancer risk estimation. A physiologically based pharmacokinetic (PB-PK) dosimetry model is used to plan animal experiments and to extrapolate nickel kinetics from animals to humans. These data are then used to estimate human lung cancer risks from human exposure to nickel aerosols. To achieve this goal, a PB-PK dosimetry model for the lung was integrated with a PB-PK dosimetry model for the internal organs. Nickel removal from the lung was found to be saturable and to follow Michaelis-Menten kinetics. The PB-PK lung dosimetry model was used to design both short-term (single exposures) and long-term (multiple intermittent exposures) needed to validate the parameters ( K m and V max) of the lung dosimetry model. A constant infusion experiment was planned using the PB-PK modeling approach to measure the distribution and elimination of intravenously administered nickel. The two PB-PK models were integrated to estimate the fate of nickel after inhalation and are being used to plan experiments for other routes of exposure such as ingestion of drinking water and dermal contact. The integrated model has been used to calculate a human cancer risk estimate in combination with short-term genotoxic experiments. Using PB-PK models in toxicology, as illustrated here, conserves experimental animals, aids in understanding new physiological phenomena (such as saturable clearance from the lung), incorporates in vitro tests with in vivo experiments, and provides a means of extrapolation to human health risks from multiple routes of exposure. Introducing the concepts of mathematical modeling into toxicity experiments at the beginning of the experiment improves the usefulness of the experiments in risk estimation. PB-PK models are suggested as a new basis for experimental design in toxicology.